Iterative matching method and system for partial fingerprint verification

ABSTRACT

An iterative matching method for partial fingerprint verification includes providing database features and input features; initially comparing the input features with the database features using one of the database features as a first reference point, resulting in initial matched feature pairs between initial matched database features and corresponding initial matched input features; and progressively comparing the input features with the database features using a gravity center of the initial matched database features as a second reference point, resulting in progressive matched feature pairs between progressive matched database features and corresponding progressive matched input features.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to fingerprint verification, andmore particularly to an iterative matching method and system for partialfingerprint verification.

2. Description of Related Art

Fingerprints have been the most widely used biometrics applied as a formof identification and access control. Fingerprint verification has beengradually used in electronic devices, particularly handheld electronicdevices (e.g., mobile phones or tablet personal computers) to replacepassword verification in a more convenient and safer manner.

A fingerprint is characterized by ridges and valleys, of which a patternis uniquely made to each person. Major features of a fingerprint areminutiae that include, for example, ridge endings and ridgebifurcations. Fingerprint verification involves two stages: (1)enrolment and (2) matching. In the enrolment stage, enrolleefingerprints are acquired and stored in a database. In the matchingstage, a claimant fingerprint is compared with the enrollee fingerprint.

Some fingerprint sensors, particularly of handheld electronic devices,have a size smaller than the magnitude of a typical fingerprint. Thosefingerprint sensors, therefore, can at most detect partial informationabout the fingerprint. Accordingly, performance of fingerprintverification using the small-size fingerprint sensors would degradegreatly, compared to that using full-size fingerprint sensors.

Features (or minutiae), instead of a full fingerprint pattern, arecommonly involved in fingerprint verification. However, due to deviationof corresponding features, relation of translation and rotation of thecorresponding features usually causes feature mismatch, thereforedecreasing performance, for example, measured in terms of genuineacceptance rate (GAR).

For the reason that conventional fingerprint verification could not beeffectively performed based on a partial fingerprint input, a need hasarisen to propose a novel method for partial fingerprint verificationwith enhanced effectiveness.

SUMMARY OF THE INVENTION

In view of the foregoing, it is an object of the embodiment of thepresent invention to provide an iterative matching method and system forpartial fingerprint verification to achieve higher matching result andenhanced performance of fingerprint verification with little overhead intime.

According to one embodiment, database features and input features areprovided. Initial feature matching is performed to initially compare theinput features with the database features using one of the databasefeatures as a first reference point, resulting in initial matchedfeature pairs between initial matched database features andcorresponding initial matched input features. Progressive featurematching is performed to progressively compare the input features withthe database features using a gravity center of the initial matcheddatabase features as a second reference point, resulting in progressivematched feature pairs between progressive matched database features andcorresponding progressive matched input features.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified block diagram illustrated of a fingerprintsystem adaptable to the embodiments of the present invention;

FIG. 2 shows a flow diagram illustrated of an iterative matching methodfor partial fingerprint verification according to one embodiment of thepresent invention;

FIG. 3 shows a block diagram illustrated of an iterative matching systemfor partial fingerprint verification corresponding to the iterativematching method of FIG. 2 according to one embodiment of the presentinvention;

FIG. 4A shows exemplary database features;

FIG. 4B shows exemplary input features;

FIG. 4C shows the database features and the input features afterperforming the initial feature matching;

FIG. 5A shows a gravity center of the initial matched database featuresof FIG. 4C;

FIG. 5B shows a gravity center of the initial matched input features ofFIG. 4C; and

FIG. 5C shows the database features and the input features afterperforming the progressive feature matching.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a simplified block diagram illustrated of a fingerprintsystem 100 adaptable to the embodiments of the present invention. Thefingerprint system 100 may include a fingerprint sensor 11 that is usedto capture a fingerprint image, i.e., the digital image of a fingerprintpattern. In this specification, the term fingerprint image is usuallyabbreviated to fingerprint. Technologies adopted in the fingerprintsensor 11 may include, for example, capacitive, optical, radio frequency(RF), thermal and piezoresistive. The fingerprint image captured by thefingerprint sensor 11 is then digitally processed by a processor 12,such as a digital image processor, to extract features associated withthe captured fingerprint image. The digital image processor is a type ofdigital signal processor used for image processing in electronic devicessuch as mobile phones or tablet personal computers. The extractedfeatures are stored in a memory device (abbreviated as memoryhereinafter) 13, and are used later for matching. The memory 13 mayinclude one or more storage areas manufactured by the same or differentstorage technologies such as non-volatile memory and volatile memory. Inthe specification, the terms feature and minutiae are usedinterchangeably. Minutiae may refer to features of a fingerprint, usingwhich comparisons can be made. Minutiae may include, for example, ridgeending, ridge bifurcation, short (or independent) ridge, island, ridgeenclosure, spur, crossover (or bridge), delta and core.

FIG. 2 shows a flow diagram illustrated of an iterative matching method200 for partial fingerprint verification according to one embodiment ofthe present invention. The steps of the iterative matching method 200may be performed by an electronic circuit such as the processor 12(FIG. 1) which performs operations on data from the fingerprint sensor11 and/or memory 13, and generates outputs accordingly.

FIG. 3 shows a block diagram illustrated of an iterative matching system300 for partial fingerprint verification corresponding to the iterativematching method 200 of FIG. 2 according to one embodiment of the presentinvention. The blocks of the iterative matching system 300 may beimplemented by hardware, software or their combinations. In oneembodiment, the blocks of the iterative matching system 300 may beimplemented by the fingerprint sensor 11, the processor 12 and thememory 13, accompanied by software or instructions operable in theprocessor 12.

In step 21, a database feature set that includes database features (orenrollee features) is provided by a database feature unit 31 to create abiometric template which is stored beforehand and used for matchingafterwards. Specifically, the database features are acquired (orcaptured) by the fingerprint sensor 11, are extracted by the processor12, and are then stored in a database configured in the memory 13 forlater use.

In step 22, an input feature set that includes input features (orclaimant features) is provided by an input feature unit 32.Specifically, the input features are acquired (or captured) by thefingerprint sensor 11, are extracted by the processor 12, and are thentemporarily stored in a storage area configured in the memory 13.

In the embodiment, the database features and the input features each mayinclude data such as translation and rotation, expressed generally as(x, y, θ) where x represents translation (or coordinate) along X axis, yrepresents translation along Y axis, and θ represents rotation (e.g.,ridge angle).

In step 23, the input features of the input feature set are initiallycompared with the database features of the database feature set by aninitial feature matching unit 33, thereby performing initial featurematching. According to one aspect of the embodiment, the initial featurematching (step 23) is performed using one of the database features as a(first) reference point.

FIG. 4A shows exemplary database features denoted by circles, and FIG.4B shows exemplary input features denoted by triangles. For betterunderstanding the invention, the database feature set includes onlythree database features F1′, F2′ and F3′ expressed as (x1′, y1′, θ1′),(x2′, y2′, θ2′) and (x3′, y3′, θ3′) respectively; and the input featureset includes only three input features F1, F2 and F3 expressed as (x1,y1, θ1), (x2, y2, θ2) and (x3, y3, θ3) respectively.

Specifically speaking, in the initial feature matching (step 23),database feature F1′ is used as the reference point, and the inputfeatures F1, F2 and F3 are translated according to the translations(x1′, y1′) of the reference point F1′ and the translations (x1, y1) of acorresponding input feature F1, thereby overlapping F1 and F1′.Subsequently, the input features F1, F2 and F3 are rotated at thereference point with an angle determined by the rotation (θ1′) of thereference point F1′ and the rotation (θ1) of the corresponding inputfeature F1. The initial feature matching (step 23) may be performedusing conventional techniques, details of which are omitted herein forbrevity.

FIG. 4C shows the database features and the input features afterperforming the initial feature matching (step 23). As exemplified inFIG. 4C, there are two initial matched feature pairs (F1′, F1) and (F2′,F2) between initial matched database features (F1′, F2′) andcorresponding initial matched input features (F1, F2) as they aresituated in associated matching bounding boxes 40. In the embodiment,the matching bounding box 40 defines a range centering on an associateddatabase feature. Also exemplified in FIG. 4C, there is an initialmismatched feature pair (F3′, F3) as the input feature F3 is situatedoutside the associated matching bonding box 40. Therefore, afterperforming the initial feature matching (step 23), there are two initialmatched database features F1′ and F2′, and two initial matched inputfeatures F1 and F2. Unfortunately, there is one initial mismatcheddatabase feature F3′, and one initial mismatched input feature F3.

The initial feature matching as exemplified in FIGS. 4A-4C demonstratesonly one of many possible matching combinations. For the example shownin FIGS. 4A-4C having three database features and three input features,there may be nine (i.e., 3×3) possible matching combinations. Ingeneral, there are n×m possible matching combinations for initialfeature matching having n database features and m input features.

In step 24, the input features of the input feature set areprogressively compared with the database features of the databasefeature set by a progressive feature matching unit 34, therebyperforming progressive feature matching. According to another aspect ofthe embodiment, the progressive feature matching (step 24) is performedusing a gravity center (GC) of initial matched database features as a(second) reference point. For example, the gravity center of the initialmatched database features F1′ and F2′ is used as the reference point inthe progressive feature matching (step 24). It is noted that theprogressive feature matching (step 24) may be adapted to all matchingcombinations with at least two initial matched database features in theinitial feature matching (step 23). In another embodiment, theprogressive feature matching (step 24) may be adapted to a portion ofthe matching combinations. For example, the progressive feature matching(step 24) may be adapted to all matching combinations with at least p (pis an integer greater than 2) initial matched database features in theinitial feature matching (step 23).

FIG. 5A shows a gravity center GC′ of the initial matched databasefeatures of FIG. 4C, and FIG. 5B shows a gravity center GC of theinitial matched input features of FIG. 4C. Specifically speaking, in theprogressive feature matching (step 24), the gravity center GC′ of theinitial matched database features (e.g., F1′ and F2′) is used as thereference point, and the gravity center GC of the initial matched inputfeatures (e.g., F1 and F2) is translated according to translationdifferences between the initial matched database features and theinitial matched input features, thereby overlapping GC and GC′. In theembodiment, Δx1(=x1−x1′) denotes translation difference of the initialmatched feature pair (F1′, F1) along X axis, and Δy1(=y1−y1′) denotestranslation difference of the initial matched feature pair (F1′, F1)along Y axis. Similarly, Δx2(=x2−x2′) denotes translation difference ofthe initial matched feature pair (F2′, F2) along X axis, andΔy2(=y2−y2′) denotes translation difference of the initial matchedfeature pair (F2′, F2) along Y axis.

Subsequently, the input features F1, F2 and F3 are rotated at thereference point with an angle determined by rotation differences betweenthe initial matched database features and the initial matched inputfeatures. In the embodiment, Δθ1(=θ1−θ1′) denotes rotation difference ofthe initial matched feature pair (F1, F1′), and Δθ2(=θ2−θ2′) denotesrotation difference of the initial matched feature pair (F2, F2′).

The translation differences (e.g., Δx1, Δx2, Δy1 and Δy2) and therotation differences (e.g., Δθ1 and Δθ2) may be obtained by anadjustment unit 35 (FIG. 3). In addition to the translation differencesand the rotation differences, the adjustment unit 35 may furthergenerate an average translation difference that is the average of thetranslation differences (e.g., (Δx1+Δx2)/2 and (Δy1+Δy2)/2), and maygenerate an average rotation difference that is the average of therotation differences (e.g., (Δθ1+Δθ2)/2). Accordingly, the gravitycenter GC of the initial matched input features (e.g., F1 and F2) istranslated to overlap the gravity center GC′ of the initial matcheddatabase features (e.g., F1′ and F2′) according to the averagetranslation differences (i.e., (Δx1+Δx2)/2 and (Δy1+Δy2)/2), followed byrotating at the reference point with an angle equal to the averagerotation difference (i.e., (Δθ1+Δθ2)/2).

FIG. 5C shows the database features and the input features afterperforming the progressive feature matching (step 24). As exemplified inFIG. 5C, in addition to progressive matched feature pairs (F1′, F1) and(F2′, F2), feature pair (F3′, F3) also becomes a progressive matchedfeature pair as the input feature F3 is now situated inside theassociated matching bonding box 40 due to progressive feature matching(step 24), thereby raising matching result and enhancing performance offingerprint verification. Therefore, after performing the progressivefeature matching (step 24), there are three progressive matched databasefeatures F1′, F2′ and F3′, and three progressive matched input featuresF1, F2 and F3.

As shown in FIG. 2, the progressive feature matching (step 24) isiteratively performed unless the number Mi of the progressive matchedfeature pairs is not greater than the number N of the initial matchedfeature pairs or the number Mi−1 of the previously progressive matchedfeature pairs. Alternatively speaking, the progressive feature matching(step 24) is iteratively performed to raise matching result and enhanceperformance of fingerprint verification, until the number Mi of theprogressive matched feature pairs no longer increases.

Although specific embodiments have been illustrated and described, itwill be appreciated by those skilled in the art that variousmodifications may be made without departing from the scope of thepresent invention, which is intended to be limited solely by theappended claims.

What is claimed is:
 1. An iterative matching method for partialfingerprint verification, comprising: providing a plurality of databasefeatures; providing a plurality of input features; performing initialfeature matching to initially compare the input features with thedatabase features using one of the database features as a firstreference point, resulting in initial matched feature pairs betweeninitial matched database features and corresponding initial matchedinput features; and performing progressive feature matching toprogressively compare the input features with the database featuresusing a gravity center of the initial matched database features as asecond reference point, resulting in progressive matched feature pairsbetween progressive matched database features and correspondingprogressive matched input features; wherein the step of performing theprogressive feature matching is iteratively performed unless a number ofthe progressive matched feature pairs is not greater than a number ofthe initial matched feature pairs or a number of the progressive matchedfeature pairs obtained in a previously performed progressive featurematching.
 2. The method of claim 1, wherein each of the databasefeatures and the input features includes translation and rotation. 3.The method of claim 2, in the initial feature matching, wherein theinput features are translated according to the translation of the firstreference point and the translation of a corresponding input feature. 4.The method of claim 3, in the initial feature matching, wherein theinput features are rotated at the first reference point with an angledetermined by the rotation of the first reference point and the rotationof the corresponding input feature.
 5. The method of claim 1, in theprogressive feature matching, wherein a gravity center of the initialmatched input features is translated according to translationdifferences between the initial matched database features and theinitial matched input features.
 6. The method of claim 5, wherein thegravity center of the initial matched input features is translatedaccording to an average translation difference that is an average of thetranslation differences.
 7. The method of claim 5, in the progressivefeature matching, wherein the input features are rotated at the secondreference point with an angle determined by rotation differences betweenthe initial matched database features and the initial matched inputfeatures.
 8. The method of claim 7, wherein the input features arerotated at the second reference point with the angle determined by anaverage rotation difference that is an average of the rotationdifferences.
 9. An iterative matching system for partial fingerprintverification, comprising: a database feature unit that provides aplurality of database features; an input feature unit that provides aplurality of input features; an initial feature matching unit thatperforms initial feature matching to initially compare the inputfeatures with the database features using one of the database featuresas a first reference point, resulting in initial matched feature pairsbetween initial matched database features and corresponding initialmatched input features; and a progressive feature matching unit thatperforms progressive feature matching to progressively compare the inputfeatures with the database features using a gravity center of theinitial matched database features as a second reference point, resultingin progressive matched feature pairs between progressive matcheddatabase features and corresponding progressive matched input features;wherein the progressive feature matching unit iteratively performs theprogressive feature matching unless a number of the progressive matchedfeature pairs is not greater than a number of the initial matchedfeature pairs or a number of the progressive matched feature pairsobtained in a previously performed progressive feature matching.
 10. Thesystem of claim 9, wherein each of the database features and the inputfeatures includes translation and rotation.
 11. The system of claim 10,in the initial feature matching, wherein the input features aretranslated according to the translation of the first reference point andthe translation of a corresponding input feature.
 12. The system ofclaim 11, in the initial feature matching, wherein the input featuresare rotated at the first reference point with an angle determined by therotation of the first reference point and the rotation of thecorresponding input feature.
 13. The system of claim 9, furthercomprising an adjustment unit that generates translation differencesbetween the initial matched database features and the initial matchedinput features, according to which, in the progressive feature matching,a gravity center of the initial matched input features is translated.14. The system of claim 13, wherein the adjustment unit generates anaverage translation difference that is an average of the translationdifferences, according to which the gravity center of the initialmatched input features is translated.
 15. The system of claim 13,wherein the adjustment unit generates rotation differences between theinitial matched database features and the initial matched inputfeatures, by which an angle is determined with which, in the progressivefeature matching, the input features are rotated at the second referencepoint.
 16. The system of claim 15, wherein the adjustment unit generatesan average rotation difference that is an average of the rotationdifferences, by which the angle is determined with which the inputfeatures are rotated at the second reference point.